Exception handling is an effective mechanism to avoid unexpected runtime errors. However, novice programmers might fail to handle exceptions properly, causing serious errors like system crashing or resource leaking. In this paper, we introduce FuzzyCatch, a code recommendation tool for handling exceptions. Based on fuzzy logic, FuzzyCatch can predict if a runtime exception would occur in a given code snippet and recommend code to handle that exception. FuzzyCatch is implemented as a plugin for Android Studio. The empirical evaluation suggests that FuzzyCatch is highly effective. For example, it has top-1 accuracy of 77% on recommending what exception to catch in a try catch block and of 70% on recommending what method should be called when such an exception occurs. FuzzyCatch also achieves a high level of accuracy and outperforms baselines significantly on detecting and fixing real exception bugs.

The current state-of-the-art methods in code recommendation mostly take the crowd-based approach. The basic idea is to collect and extract code patterns from a large pool of available source code and use those code patterns for recommendations. However, different programmers have different coding styles, levels of experience, and knowledge about libraries and frameworks, all of which cause different uses of variable names, classes, and methods. When code of different programmers is combined, such differences are blurred, which could hurt the performance of the code recommendation tool for a specific programmer. In the paper, we explore a new research direction in code recommendation which focuses on personal coding patterns of programmers. As a proof of concept, we have developed a personalized code recommendation model for suggesting variable declaration and initialization code. Our techniques learn personalized code patterns for each programmer based on their coding history. The preliminary evaluation shows that our recommendation model is highly effective. For example, when evaluating on a programmer, our approach has top-1 accuracy of 62% and top-3 accuracy of 70% on recommending declaration types. Our approach has top-1 and top-3 accuracy of 67% and 76%, respectively, on recommending initialization method sequences. Furthermore, our model also outperforms the baselines significantly in these experiments.

Exception handling is an advanced programming technique to prevent run-time errors or crashes for modern software systems. However, inexperienced programmers might fail to write proper exception handling code in their programs. In this paper, we introduce ExAssist, a code recommendation tool for exception handling. ExAssist can predict what types of exception could occur in a given piece of code and recommend proper exception handling code for such exceptions. Preliminary evaluation of ExAssist suggests that it provides highly accurate recommendations. The tool's website is at rebrand.ly/exassist. The video demo of ExAssist could be found at rebrand.ly/exassistdemo.